C# (CSharp) Amazon.MachineLearning.Model Namespace

Nested Namespaces

Amazon.MachineLearning.Model.Internal

Classes

Name	Description
AddTagsRequest	Container for the parameters to the AddTags operation. Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, `AddTags` updates the tag's value.
AddTagsResponse	Amazon ML returns the following elements.
BatchPrediction	Represents the output of a `GetBatchPrediction` operation. The content consists of the detailed metadata, the status, and the data file information of a `Batch Prediction`.
CreateBatchPredictionRequest	Container for the parameters to the CreateBatchPrediction operation. Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a `DataSource`. This operation creates a new `BatchPrediction`, and uses an `MLModel` and the data files referenced by the `DataSource` as information sources. `CreateBatchPrediction` is an asynchronous operation. In response to `CreateBatchPrediction`, Amazon Machine Learning (Amazon ML) immediately returns and sets the `BatchPrediction` status to `PENDING`. After the `BatchPrediction` completes, Amazon ML sets the status to `COMPLETED`. You can poll for status updates by using the GetBatchPrediction operation and checking the `Status` parameter of the result. After the `COMPLETED` status appears, the results are available in the location specified by the `OutputUri` parameter.
CreateBatchPredictionResponse	Represents the output of a `CreateBatchPrediction` operation, and is an acknowledgement that Amazon ML received the request. The `CreateBatchPrediction` operation is asynchronous. You can poll for status updates by using the `>GetBatchPrediction` operation and checking the `Status` parameter of the result.
CreateDataSourceFromRDSRequest	Container for the parameters to the CreateDataSourceFromRDS operation. Creates a `DataSource` object from an Amazon Relational Database Service (Amazon RDS). A `DataSource` references data that can be used to perform `CreateMLModel`, `CreateEvaluation`, or `CreateBatchPrediction` operations. `CreateDataSourceFromRDS` is an asynchronous operation. In response to `CreateDataSourceFromRDS`, Amazon Machine Learning (Amazon ML) immediately returns and sets the `DataSource` status to `PENDING`. After the `DataSource` is created and ready for use, Amazon ML sets the `Status` parameter to `COMPLETED`. `DataSource` in the `COMPLETED` or `PENDING` state can be used only to perform `>CreateMLModel`>, `CreateEvaluation`, or `CreateBatchPrediction` operations. If Amazon ML cannot accept the input source, it sets the `Status` parameter to `FAILED` and includes an error message in the `Message` attribute of the `GetDataSource` operation response.
CreateDataSourceFromRDSResponse	Represents the output of a `CreateDataSourceFromRDS` operation, and is an acknowledgement that Amazon ML received the request. The `CreateDataSourceFromRDS`> operation is asynchronous. You can poll for updates by using the `GetBatchPrediction` operation and checking the `Status` parameter. You can inspect the `Message` when `Status` shows up as `FAILED`. You can also check the progress of the copy operation by going to the `DataPipeline` console and looking up the pipeline using the `pipelineId` from the describe call.
CreateDataSourceFromRedshiftRequest	Container for the parameters to the CreateDataSourceFromRedshift operation. Creates a `DataSource` from a database hosted on an Amazon Redshift cluster. A `DataSource` references data that can be used to perform either `CreateMLModel`, `CreateEvaluation`, or `CreateBatchPrediction` operations. `CreateDataSourceFromRedshift` is an asynchronous operation. In response to `CreateDataSourceFromRedshift`, Amazon Machine Learning (Amazon ML) immediately returns and sets the `DataSource` status to `PENDING`. After the `DataSource` is created and ready for use, Amazon ML sets the `Status` parameter to `COMPLETED`. `DataSource` in `COMPLETED` or `PENDING` states can be used to perform only `CreateMLModel`, `CreateEvaluation`, or `CreateBatchPrediction` operations. If Amazon ML can't accept the input source, it sets the `Status` parameter to `FAILED` and includes an error message in the `Message` attribute of the `GetDataSource` operation response. The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a `SelectSqlQuery` query. Amazon ML executes an `Unload` command in Amazon Redshift to transfer the result set of the `SelectSqlQuery` query to `S3StagingLocation`. After the `DataSource` has been created, it's ready for use in evaluations and batch predictions. If you plan to use the `DataSource` to train an `MLModel`, the `DataSource` also requires a recipe. A recipe describes how each input variable will be used in training an `MLModel`. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions. You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call `GetDataSource` for an existing datasource and copy the values to a `CreateDataSource` call. Change the settings that you want to change and make sure that all required fields have the appropriate values.
CreateDataSourceFromRedshiftResponse	Represents the output of a `CreateDataSourceFromRedshift` operation, and is an acknowledgement that Amazon ML received the request. The `CreateDataSourceFromRedshift` operation is asynchronous. You can poll for updates by using the `GetBatchPrediction` operation and checking the `Status` parameter.
CreateDataSourceFromS3Request	Container for the parameters to the CreateDataSourceFromS3 operation. Creates a `DataSource` object. A `DataSource` references data that can be used to perform `CreateMLModel`, `CreateEvaluation`, or `CreateBatchPrediction` operations. `CreateDataSourceFromS3` is an asynchronous operation. In response to `CreateDataSourceFromS3`, Amazon Machine Learning (Amazon ML) immediately returns and sets the `DataSource` status to `PENDING`. After the `DataSource` has been created and is ready for use, Amazon ML sets the `Status` parameter to `COMPLETED`. `DataSource` in the `COMPLETED` or `PENDING` state can be used to perform only `CreateMLModel`, `CreateEvaluation` or `CreateBatchPrediction` operations. If Amazon ML can't accept the input source, it sets the `Status` parameter to `FAILED` and includes an error message in the `Message` attribute of the `GetDataSource` operation response. The observation data used in a `DataSource` should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the `DataSource`. After the `DataSource` has been created, it's ready to use in evaluations and batch predictions. If you plan to use the `DataSource` to train an `MLModel`, the `DataSource` also needs a recipe. A recipe describes how each input variable will be used in training an `MLModel`. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.
CreateDataSourceFromS3Response	Represents the output of a `CreateDataSourceFromS3` operation, and is an acknowledgement that Amazon ML received the request. The `CreateDataSourceFromS3` operation is asynchronous. You can poll for updates by using the `GetBatchPrediction` operation and checking the `Status` parameter.
CreateEvaluationRequest	Container for the parameters to the CreateEvaluation operation. Creates a new `Evaluation` of an `MLModel`. An `MLModel` is evaluated on a set of observations associated to a `DataSource`. Like a `DataSource` for an `MLModel`, the `DataSource` for an `Evaluation` contains values for the `Target Variable`. The `Evaluation` compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the `MLModel` functions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding `MLModelType`: `BINARY`, `REGRESSION` or `MULTICLASS`. `CreateEvaluation` is an asynchronous operation. In response to `CreateEvaluation`, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to `PENDING`. After the `Evaluation` is created and ready for use, Amazon ML sets the status to `COMPLETED`. You can use the `GetEvaluation` operation to check progress of the evaluation during the creation operation.
CreateEvaluationResponse	Represents the output of a `CreateEvaluation` operation, and is an acknowledgement that Amazon ML received the request. `CreateEvaluation` operation is asynchronous. You can poll for status updates by using the `GetEvcaluation` operation and checking the `Status` parameter.
CreateMLModelRequest	Container for the parameters to the CreateMLModel operation. Creates a new `MLModel` using the `DataSource` and the recipe as information sources. An `MLModel` is nearly immutable. Users can update only the `MLModelName` and the `ScoreThreshold` in an `MLModel` without creating a new `MLModel`. `CreateMLModel` is an asynchronous operation. In response to `CreateMLModel`, Amazon Machine Learning (Amazon ML) immediately returns and sets the `MLModel` status to `PENDING`. After the `MLModel` has been created and ready is for use, Amazon ML sets the status to `COMPLETED`. You can use the `GetMLModel` operation to check the progress of the `MLModel` during the creation operation. `CreateMLModel` requires a `DataSource` with computed statistics, which can be created by setting `ComputeStatistics` to `true` in `CreateDataSourceFromRDS`, `CreateDataSourceFromS3`, or `CreateDataSourceFromRedshift` operations.
CreateMLModelResponse	Represents the output of a `CreateMLModel` operation, and is an acknowledgement that Amazon ML received the request. The `CreateMLModel` operation is asynchronous. You can poll for status updates by using the `GetMLModel` operation and checking the `Status` parameter.
CreateRealtimeEndpointResponse	Represents the output of an `CreateRealtimeEndpoint` operation. The result contains the `MLModelId` and the endpoint information for the `MLModel`. The endpoint information includes the URI of the `MLModel`; that is, the location to send online prediction requests for the specified `MLModel`.
DataSource	Represents the output of the `GetDataSource` operation. The content consists of the detailed metadata and data file information and the current status of the `DataSource`.
DeleteBatchPredictionResponse	Represents the output of a `DeleteBatchPrediction` operation. You can use the `GetBatchPrediction` operation and check the value of the `Status` parameter to see whether a `BatchPrediction` is marked as `DELETED`.
DeleteDataSourceResponse	Represents the output of a `DeleteDataSource` operation.
DeleteEvaluationRequest	Container for the parameters to the DeleteEvaluation operation. Assigns the `DELETED` status to an `Evaluation`, rendering it unusable. After invoking the `DeleteEvaluation` operation, you can use the `GetEvaluation` operation to verify that the status of the `Evaluation` changed to `DELETED`. Caution The results of the `DeleteEvaluation` operation are irreversible.
DeleteEvaluationResponse	Represents the output of a `DeleteEvaluation` operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request. You can use the `GetEvaluation` operation and check the value of the `Status` parameter to see whether an `Evaluation` is marked as `DELETED`.
DeleteMLModelResponse	Represents the output of a `DeleteMLModel` operation. You can use the `GetMLModel` operation and check the value of the `Status` parameter to see whether an `MLModel` is marked as `DELETED`.
DeleteRealtimeEndpointResponse	Represents the output of an `DeleteRealtimeEndpoint` operation. The result contains the `MLModelId` and the endpoint information for the `MLModel`.
DeleteTagsRequest	Container for the parameters to the DeleteTags operation. Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags. If you specify a tag that doesn't exist, Amazon ML ignores it.
DeleteTagsResponse	Amazon ML returns the following elements.
DescribeBatchPredictionsRequest	Container for the parameters to the DescribeBatchPredictions operation. Returns a list of `BatchPrediction` operations that match the search criteria in the request.
DescribeBatchPredictionsResponse	Represents the output of a `DescribeBatchPredictions` operation. The content is essentially a list of `BatchPrediction`s.
DescribeEvaluationsResponse	Represents the query results from a `DescribeEvaluations` operation. The content is essentially a list of `Evaluation`.
DescribeMLModelsRequest	Container for the parameters to the DescribeMLModels operation. Returns a list of `MLModel` that match the search criteria in the request.
DescribeMLModelsResponse	Represents the output of a `DescribeMLModels` operation. The content is essentially a list of `MLModel`.
DescribeTagsRequest	Container for the parameters to the DescribeTags operation. Describes one or more of the tags for your Amazon ML object.
DescribeTagsResponse	Amazon ML returns the following elements.
Evaluation	Represents the output of `GetEvaluation` operation. The content consists of the detailed metadata and data file information and the current status of the `Evaluation`.
GetBatchPredictionResponse	Represents the output of a `GetBatchPrediction` operation and describes a `BatchPrediction`.
GetDataSourceResponse	Represents the output of a `GetDataSource` operation and describes a `DataSource`.
GetMLModelRequest	Container for the parameters to the GetMLModel operation. Returns an `MLModel` that includes detailed metadata, data source information, and the current status of the `MLModel`. `GetMLModel` provides results in normal or verbose format.
GetMLModelResponse	Represents the output of a `GetMLModel` operation, and provides detailed information about a `MLModel`.
IdempotentParameterMismatchException
InternalServerException
InvalidInputException
InvalidTagException
LimitExceededException
MLModel	Represents the output of a `GetMLModel` operation. The content consists of the detailed metadata and the current status of the `MLModel`.
PredictResponse	This is the response object from the Predict operation.
Prediction	The output from a `Predict` operation: `Details` - Contains the following attributes: `DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION \| BINARY \| MULTICLASS` `DetailsAttributes.ALGORITHM - SGD` `PredictedLabel` - Present for either a `BINARY` or `MULTICLASS` `MLModel` request. `PredictedScores` - Contains the raw classification score corresponding to each label. `PredictedValue` - Present for a `REGRESSION` `MLModel` request.
PredictorNotMountedException
RDSDataSpec	The data specification of an Amazon Relational Database Service (Amazon RDS) `DataSource`.
RealtimeEndpointInfo	Describes the real-time endpoint information for an `MLModel`.
RedshiftDataSpec	Describes the data specification of an Amazon Redshift `DataSource`.
ResourceNotFoundException
S3DataSpec	Describes the data specification of a `DataSource`.
Tag	A custom key-value pair associated with an ML object, such as an ML model.
TagLimitExceededException
UpdateBatchPredictionRequest	Container for the parameters to the UpdateBatchPrediction operation. Updates the `BatchPredictionName` of a `BatchPrediction`. You can use the `GetBatchPrediction` operation to view the contents of the updated data element.
UpdateBatchPredictionResponse	Represents the output of an `UpdateBatchPrediction` operation. You can see the updated content by using the `GetBatchPrediction` operation.
UpdateDataSourceRequest	Container for the parameters to the UpdateDataSource operation. Updates the `DataSourceName` of a `DataSource`. You can use the `GetDataSource` operation to view the contents of the updated data element.
UpdateDataSourceResponse	Represents the output of an `UpdateDataSource` operation. You can see the updated content by using the `GetBatchPrediction` operation.
UpdateEvaluationRequest	Container for the parameters to the UpdateEvaluation operation. Updates the `EvaluationName` of an `Evaluation`. You can use the `GetEvaluation` operation to view the contents of the updated data element.
UpdateEvaluationResponse	Represents the output of an `UpdateEvaluation` operation. You can see the updated content by using the `GetEvaluation` operation.
UpdateMLModelRequest	Container for the parameters to the UpdateMLModel operation. Updates the `MLModelName` and the `ScoreThreshold` of an `MLModel`. You can use the `GetMLModel` operation to view the contents of the updated data element.
UpdateMLModelResponse	Represents the output of an `UpdateMLModel` operation. You can see the updated content by using the `GetMLModel` operation.